pybamm

name	pybamm
description	Use when the user is working with physics-based battery modeling, lithium-ion or lead-acid models, SPM/SPMe/DFN/MSMR/MPM models, battery experiments, charge/discharge protocols, degradation models, parameter sets, PyBaMM Simulation objects, processed battery variables, or solver/mesh choices for electrochemical battery simulations. Prefer PyBaMM over generic ODE solvers when the task is battery model setup, simulation, comparison, or analysis.
version	0.1.0
compatible_versions	>=25.0,<27
related_skills	["impedance"]
canonical_docs	https://docs.pybamm.org/en/stable/
canonical_tutorials	https://docs.pybamm.org/en/stable/source/user_guide/getting_started.html

PyBaMM

What this library is for

PyBaMM is a battery modeling package for physics-based electrochemical models, especially lithium-ion and lead-acid cells. It provides built-in models, parameter sets, experiments, solvers, processed variables, plotting, and a high level Simulation workflow that handles parameter processing, discretization, solving, and output management.

When to use this vs. alternatives

• Use PyBaMM for SPM, SPMe, DFN, MPM, MSMR, Thevenin/equivalent-circuit, lead acid, degradation, thermal, experiment, parameter-study, and solver workflows for batteries.
• Use impedance.py for measured EIS equivalent-circuit fitting. Use PyBaMM when the impedance or voltage response should come from a physics-based battery model or PyBaMM-compatible parameterization workflow.
• Use SciPy ODE/DAE solvers directly only when developing a custom model outside PyBaMM's expression tree, battery submodels, and discretization framework.
• Use PyBOP or another optimization layer when the task is parameter inference on top of PyBaMM models; keep PyBaMM as the forward simulator.
• Do not hand-code DFN/SPM equations for normal agent tasks. PyBaMM already encodes model equations, events, units, parameters, discretization, and solver choices.

Canonical workflow

Use Simulation first. It is the documented high-level path for model setup, parameter processing, discretization, solving, and plotting.

import pybamm

model = pybamm.lithium_ion.DFN()
parameter_values = pybamm.ParameterValues("Chen2020")

experiment = pybamm.Experiment(
    [
        (
            "Discharge at C/10 for 10 hours or until 3.3 V",
            "Rest for 1 hour",
            "Charge at 1 A until 4.1 V",
            "Hold at 4.1 V until 50 mA",
            "Rest for 1 hour",
        )
    ]
    * 3
)

sim = pybamm.Simulation(
    model,
    parameter_values=parameter_values,
    experiment=experiment,
)
solution = sim.solve()

voltage = solution["Terminal voltage [V]"]
current = solution["Current [A]"]
capacity = solution["Discharge capacity [A.h]"]

print("termination", solution.termination)
print("final_voltage", voltage.entries[-1])
print("final_capacity_Ah", capacity.entries[-1])
sim.plot(["Terminal voltage [V]", "Current [A]", "Discharge capacity [A.h]"])

For deeper examples, read:

• Getting started: https://docs.pybamm.org/en/stable/source/user_guide/getting_started.html
• Tutorial 1, run a model: https://docs.pybamm.org/en/stable/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.html
• Experiments: https://docs.pybamm.org/en/stable/source/examples/notebooks/getting_started/tutorial-5-run-experiments.html
• Simulation class: https://docs.pybamm.org/en/stable/source/examples/notebooks/simulations_and_experiments/simulation-class.html

Key conventions and gotchas

• sim.solve([0, 3600]) uses seconds for explicit time windows. Experiment strings use battery-domain units such as C/10, 1 A, 3.3 V, and durations like 10 hours.
• Model choice matters. SPM is fast and simplified, SPMe adds electrolyte effects, DFN is fuller and slower, and degradation/thermal options add physics and stiffness. Do not default to DFN if the user needs fast sweeps.
• Parameter sets are part of the scientific claim. Record the named parameter set and every parameter_values.update(...) override.
• PyBaMM variable names are exact strings with units, e.g. "Terminal voltage [V]". Inspect solution.all_models[0].variables.keys() or the docs instead of guessing names.
• Solver availability depends on installation. The conda-forge distribution may not include every optional solver such as IDAKLU; handle solver import or availability errors explicitly.
• Events and experiment termination are meaningful outputs. Check solution.termination and whether voltage/SOC/capacity limits were hit before interpreting curves.
• Initial SOC and stoichiometry are not interchangeable with arbitrary initial concentrations. Use documented parameter helpers when setting initial stoichiometries or long degradation experiments.

Anti-patterns

• Do not build a manual mesh/discretization/solver pipeline for a basic simulation. Use pybamm.Simulation unless the user explicitly asks to inspect internals.
• Do not mix current sign conventions casually. Use PyBaMM Experiment strings or documented variables, and plot current/voltage to confirm the protocol.
• Do not compare SPM, SPMe, and DFN outputs without using the same parameter set, experiment, initial SOC, solver tolerances, and output variables.
• Do not fit parameters by changing random dictionary keys until curves match. Record parameter names, units, bounds, data provenance, and use an inference tool when appropriate.
• Do not trust a long degradation simulation without checking event termination, solver warnings, time-step behavior, and physically plausible capacity/SOH trends.

Diagnostic checks

Before trusting outputs, the agent should:

• Log PyBaMM version, model class/options, parameter set, experiment definition, solver, time window, and output variable names.
• Plot terminal voltage, current, temperature if enabled, capacity/SOC, and any degradation variables relevant to the question.
• Check solution.termination, solver warnings/errors, and whether the intended event or time horizon ended the simulation.
• Compare model outputs against expected voltage/current ranges and battery limits before reporting physical conclusions.
• For parameter studies, vary one controlled input at a time and keep the same model, parameter set, mesh, solver, and experiment.
• Save the script/notebook and explicit parameter overrides for reproducibility.

Pointers to deeper material

• Documentation: https://docs.pybamm.org/en/stable/
• Getting started: https://docs.pybamm.org/en/stable/source/user_guide/getting_started.html
• Example notebooks: https://docs.pybamm.org/en/stable/source/examples/index.html
• Source repository: https://github.com/pybamm-team/PyBaMM
• PyBaMM site: https://pybamm.org/
• Paper: Sulzer et al. (2021), "Python Battery Mathematical Modelling (PyBaMM)", Journal of Open Research Software 9(1), 14. https://doi.org/10.5334/jors.309